A compact deep learning model for khmer handwritten text recognition
The motivation of this study is to develop a compact offline recognition model for Khmer handwritten text that would be successfully applied under limited access to high-performance computational hardware. Such a task aims to ease the ad-hoc digitization of vast handwritten archives in many spheres....
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| 主要な著者: | , |
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| フォーマット: | 論文 |
| 言語: | English |
| 出版事項: |
Institute of Advanced Engineering and Science
2021
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| 主題: | |
| オンライン･アクセス: | http://eprints.utm.my/id/eprint/94987/1/NorlizaNoor2021_ACompactDeepLearningModel.pdf http://eprints.utm.my/id/eprint/94987/ http://dx.doi.org/10.11591/ijai.v10.i3.pp584-591 |
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| 要約: | The motivation of this study is to develop a compact offline recognition model for Khmer handwritten text that would be successfully applied under limited access to high-performance computational hardware. Such a task aims to ease the ad-hoc digitization of vast handwritten archives in many spheres. Data collected for previous experiments were used in this work. The one-against-all classification was completed with state-of-the-art techniques. A compact deep learning model (2+1CNN), with two convolutional layers and one fully connected layer, was proposed. The recognition rate came out to be within 93-98%. The compact model is performed on par with the state-of-the-art models. It was discovered that computational capacity requirements usually associated with deep learning can be alleviated, therefore allowing applications under limited computational power. |
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